Field of the Invention
The present invention is in the technical field of contour detection and identification within file format data types for use in pattern recognition analysis.
Description of the Related Art
Image detection algorithms locating objects (people, mechanical, signal waveform representations, or objects of any physical nature), within data formats, identify the objects for a possible purpose of tracking the object within the image. They do so whether objects are stationary (image that does not change in time or frequency) or dynamic (change in time or frequency); that is, they find an image object, but do not take the objects found outside of the source for further processing. As a result, current technologies, in image processing analysis work, desires the computational process to remain with the original source when performing calculations for any attempts to make object identification possible.
Current technologies chose not to remove the image objects searched for from the data set so that objects found may be portable to other applications that may wish to further analyze the image data instances. Current technology makes no attempt to transform the image it finds into another numerical quantity source that is not object related in a typical sense; that is, current technology does not talk about an object as an equation, it speaks of an identification processed by showing the processed image within a modified version of its own data format source. Current technology stays within the realm of the image and talks about any identifications that need to be stated to a user of such a system as a reference to the original data set and its data values from where identifications came.
There are no current methods that use metrics of recognizable and unrecognizable patterns, of as small as 1 pixel, to identify an object, instead. There are no systems that use such methods to group such patterns so that a pattern of one dimension can be paired to an entirely different dimensioned pattern in an effort to identify an object in still another dimension. There are no current methods that use a pattern as a collection of metrics to identify an object, whether or not the final identified object is identifiable by human visual experiences or expectations, and make that representation portable to other entirely different computer system designs. There are no current methods that use a pattern grouping method, whose output from a system of hardware is portable and independent of the source to define an object and fingerprint it without having to reuse irrelevant data of the source. No current method uses contours, created from contour maps of data sets (typically associated to the study of topography) to create contour metrics for a new type of system now introduced as a learning contour identification system.
These novel metrics herein are called contour metrics and are derived from contours of contour mappings, where each contour of the map has its own set of metrics stored as container sets usable by the design of the learning contour identification system. The metrics of the container sets are typically statistical density sets, areas sets, coordinate point sets, and other metrics created and determined by a system of hardware components that make up a learning object identification system. Other container sets are subsets of the same, or other analysis sets that could very well be the output of a mathematical processes, machine code instruction sets, or subsets of its own container set. The containers group together to define the objects or the groups of objects, and to essentially leave out irrelevant information of the data source for the benefit of pattern localization and final labeling. All decisions and conversions and storage locations in memory is determined by the learning contour identification system by creating a new, if you will, mathematical representation of the patterns. Essentially, the containers, then, by supplying the metrics as memory location elements, or variables (metrics of the individual containers) make the learning identification system a function processor as the metrics are plug-in modules to a learning contour identification system to make it perform a precise way that it also determines autonomously. Basically, the system and its metrics create its own encryption code set to describe a data case that has micro patterns that are found to re-occur in sets of data cases having similar data pattern representations only recognizable by the learning system hardware that created it.
The current technology does little for the purpose of further mathematical or statistical analysis on what can be learned from the object after an object is found like on a line. Current technology may identify a line, but does not provide a searchable set of metrics on that line that has relevance to the image it came from. Current technology, therefore, cannot allow the user to walk away from the source image with some pattern, in hand, as a completely different translation but having the same identification and same meaning to the application using the information detected. Current technology prefers the user and the applications using the data to remain close with source, and requires the system to show the user the object found within the source file, or to use the source file as a reference. Current technology does not attempt to “transform” an object into another quantity so that it can leave its data format environment and still have an object identity. Current technology does not attempt to provide a user with a process form derived entirely from hardware and its application software control, which not only identifies the shape, but fingerprints the pattern by a sequence of metric representations of a pattern.